Optimal and Bounded-Suboptimal Multi-Goal Task Assignment and Path Finding

Zhong, Xingfu; Li, Jiaoyang; Koenig, Sven; Ma, Hang

doi:10.1109/icra46639.2022.9812020

Cited by 16 publications

(7 citation statements)

References 23 publications

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“…For our ongoing research, we are currently developing a deeper theoretical understanding of using MAPF algorithms for long‐term autonomy of such systems (Ma 2021), a learning‐based distributed MAPF algorithm (Ma, Luo, and Ma 2021), and algorithms that can jointly solve MAPF and complex task‐planning problems (Zhong et al. 2022; Xu et al. 2022).…”

Section: Discussionmentioning

confidence: 99%

“…We outlined four directions that generalize task-assignment and MAPF research to real-world applications of large-scale multi-agent systems. For our ongoing research, we are currently developing a deeper theoretical understanding of using MAPF algorithms for long-term autonomy of such systems (Ma 2021), a learning-based distributed MAPF algorithm (Ma, Luo, and Ma 2021), and algorithms that can jointly solve MAPF and complex task-planning problems (Zhong et al 2022;Xu et al 2022). We hope that researchers working in this area can benefit from the insights provided in this article.…”

Section: Discussionmentioning

confidence: 99%

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Intelligent planning for large‐scale multi‐agent systems

2022

AI Magazine

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This article summarizes the New Faculty Highlights talk with the same title at AAAI 2021. Intelligent agents such as different types of robots will soon become an integral part of our daily lives. In real-world multi-agent systems, the most fundamental challenges are assigning tasks to multiple agents (tasklevel coordination problems) and planning collision-free paths for the agents to task locations (motion-level coordination problems). This article surveys four directions of our research on using intelligent planning techniques for the above multi-agent coordination problems. Link to video abstract: https://youtu.be/ HDAFcatq9_I

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Intelligent planning for large‐scale multi‐agent systems

2022

AI Magazine

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“…CBS-TA and ECBS-TA CBS-TA (Hönig et al 2018), inspiring many extensions (Ren, Rathinam, and Choset 2023;Zhong et al 2022;Chen et al 2021;Okumura and Défago 2023), operates on the following principle: a fixed TA transforms a TAPF instance to a MAPF instance, and CBS can solve each MAPF instance with one CT. CBS-TA efficiently explores all nodes of the different CTs (CT forest) by enumerating every TA solution. In CBS-TA, TA solutions are derived from an N × M cost matrix M c , which records the path costs from agents' start locations to targets, without considering any constraints.…”

Section: Combined Target-assignment and Path-finding (Tapf)mentioning

confidence: 99%

ITA-ECBS: A Bounded-Suboptimal Algorithm for Combined Target-Assignment and Path-Finding Problem

Tang,

Koenig,

2024

SOCS

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Multi-Agent Path Finding (MAPF), i.e., finding collision-free paths for multiple robots, plays a critical role in many applications. Sometimes, assigning a target to each agent also presents a challenge. The Combined Target-Assignment and Path-Finding (TAPF) problem, a variant of MAPF, requires one to simultaneously assign targets to agents and plan collision-free paths for agents. Several algorithms, including CBM, CBS-TA, and ITA-CBS, optimally solve the TAPF problem, with ITA-CBS being the leading algorithm for minimizing flowtime. However, the only existing bounded-suboptimal algorithm ECBS-TA is derived from CBS-TA rather than ITA-CBS. So, it faces the same issues as CBS-TA, such as searching through multiple constraint trees and spending too much time on finding the next-best target assignment. We introduce ITA-ECBS, the first bounded-suboptimal variant of ITA-CBS. Transforming ITA-CBS to its bounded-suboptimal variant is challenging because different constraint tree nodes can have different assignments of targets to agents. ITA-ECBS uses focal search to achieve efficiency and determines target assignments based on a new lower bound matrix. We show that it runs faster than ECBS-TA in 87.42% of 54,033 test cases.

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“…MG-MAPF [110] assumes that each robot is preassigned multiple unordered goal vertices and aims to compute collision-free paths for the robots to visit all goal vertices. MG-TAPF [111] aims to find a one-to-one mapping from the given tasks that each consists of a sequence of ordered goal vertices and collision-free paths for the robots that visit the goal vertices of their assigned tasks in the specified order.…”

Section: Mapf With Continuous Time or Kinematic Constraints Mapf Withmentioning

confidence: 99%

Graph-Based Multi-Robot Path Finding and Planning

2022

Curr Robot Rep

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Purpose of Review Planning collision-free paths for multiple robots is important for real-world multi-robot systems and has been studied as an optimization problem on graphs, called Multi-Agent Path Finding (MAPF). This review surveys different categories of classic and stateof-the-art MAPF algorithms and different research attempts to tackle the challenges of generalizing MAPF techniques to real-world scenarios. Recent Findings Solving MAPF problems optimally is computationally challenging. Recent advances have resulted in MAPF algorithms that can compute collision-free paths for hundreds of robots and thousands of navigation tasks in seconds of runtime. Many variants of MAPF have been formalized to adapt MAPF techniques to different real-world requirements, such as considerations of robot kinematics, online optimization for real-time systems, and the integration of task assignment and path planning. Summary Algorithmic techniques for MAPF problems have addressed important aspects of several multi-robot applications, including automated warehouse fulfillment and sortation, automated train scheduling, and navigation of non-holonomic robots and quadcopters. This showcases their potential for real-world applications of large-scale multi-robot systems.

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Optimal and Bounded-Suboptimal Multi-Goal Task Assignment and Path Finding

Cited by 16 publications

References 23 publications

Intelligent planning for large‐scale multi‐agent systems

Intelligent planning for large‐scale multi‐agent systems

ITA-ECBS: A Bounded-Suboptimal Algorithm for Combined Target-Assignment and Path-Finding Problem

Graph-Based Multi-Robot Path Finding and Planning

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